Automation of tree-ring detection and measurements using deep learning

O_LIWe present an implementable neural network-based automated detection and measurement of tree-ring boundaries from coniferous species. C_LIO_LIWe trained our Mask R-CNN extensively on over 8,000 manually annotated rings. We assessed the performance of the trained model from our core processing pipeline on real world data. C_LIO_LIThe CNN performed well, recognizing over 99% of ring boundaries (precision) and a recall value of 95% when tested on real world data. Additionally, we have implemented automatic measurements based on minimum distance between rings. With minimal editing for missed ring detections, these measurements were a 99% match with human measurements of the same samples. C_LIO_LIOur CNN is readily deployable through a Docker container and requires only basic command line skills. Application outputs include editable annotations which facilitate the efficient generation of ring-width measurements from tree-ring samples, an important source of environmental data. C_LI